Abstract
BackgroundProtein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.ResultsIn this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in Saccharomyces cerevisiae (budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.ConclusionGAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells.
Highlights
Protein-Protein Interactions (PPIs) play important roles in many biological functions
Performance of the Gram-bAsed Interaction Analysis (GAIA) algorithm To evaluate the performance of our algorithm, we tested the GAIA algorithm against both gold-standard positive and negative PPI data sets by setting the length of n-gram to 4 and the threshold of Domain-Domain Interactions (DDIs)'s hits to 8.3
We found that association method (AM) achieved a sensitivity of 51% with a specificity of 79% and maximum likelihood estimation approach (MLE) achieved a sensitivity of 57% with a specificity of 79% when tested against our gold-standard data set, proving that protein sequence information combined with structural information derived from iPfam is a better indicator to predict DDIs
Summary
Protein-Protein Interactions (PPIs) play important roles in many biological functions. Owing to advances in large-scale techniques such as the yeast two-hybrid system and affinity purification followed by mass spectrometry, interactomes of several model organisms such as Saccharomyces cerevisiae [1,2,3,4,5,6], Drosophila melanogaster [7,8] and Caenorhabditis elegans [9] have recently been extensively studied While such large-scale interaction data sets provide tremendous opportunities for data exploration there are limitations: 1) the experimental techniques for detecting PPIs are time-consuming, costly and labour intensive; 2) the quality of certain datasets is uneven; and 3) technical limitations such as the requirement to tag proteins of interest still exist. Computational approaches that identify DDIs have been studied intensively for years yielding some interesting results
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